About the Team & Role

• Own end-to-end systems architecture across data pipelines, AI/ML platforms, semantic layers, and application interfaces — designing for modularity, scale, and durability from day one.
• Build and evolve the data integration layer: ingestion, normalization, and orchestration across structured and unstructured sources, using API-first design principles (REST, GraphQL, gRPC) and real-time streaming technologies like Kafka and Apache Pulsar.
• Architect the semantic intelligence layer: knowledge graphs, ontology design, vector embeddings, and RAG techniques that give Auger context-aware reasoning across the full enterprise data fabric.
• Design and operate scalable AI/ML platforms for training, deployment, and model lifecycle management — integrating LLMs, embeddings, and multimodal models into production applications via MLOps tooling (MLflow, SageMaker, Databricks).
• Drive AI into the application layer: partner with product and design to ship agentic, adaptive user experiences that surface intelligence at the moment operators need it.
• Set architectural direction across the platform: make layer boundaries, evolution strategies, and tradeoffs explicit — and document decisions the team can execute against with confidence.
• Raise the bar on operational rigor: fault tolerance, high availability, observability, and performance at enterprise scale are non-negotiable properties, not afterthoughts.
• Mentor engineers on system design, coding standards, and operational excellence — and hold a high bar on what ships.

• Bachelor or Master's degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in systems architecture, software engineering, and platform development — with a proven track record building scalable data platforms or AI-driven systems at enterprise scale.
• Deep programming expertise in Python, Java, or C++, and hands-on experience building distributed systems in cloud-native environments (Azure, AWS, or GCP, including multi-cloud).
• Fluency in real-time data processing and analytics frameworks (Spark, Kafka, Flink) and big data technologies (Databricks, Snowflake, Hadoop).
• Advanced understanding of semantic modeling, knowledge graphs, and ontology design — including graph databases, graph embeddings, link prediction, and GNNs.
• Hands-on experience with AI/ML pipeline design and deployment, including frameworks such as TensorFlow, PyTorch, or equivalent, and familiarity with architectural patterns including microservices, event-driven architectures, and domain-driven design.
• Technical leadership through ambiguity: you set direction, communicate tradeoffs clearly to technical and non-technical partners, and write crisp architecture decisions when the stakes are high.

CodaMetrix is revolutionizing Revenue Cycle Management with its AI-powered autonomous coding solution, a multi-specialty AI-platform that translates clinical information into accurate sets of medical codes. CodaMetrix’s autonomous coding drives efficiency under fee-for-service and value-based care models and supports improved patient care. We are passionate about getting physicians and healthcare providers away from the keyboard and back to clinical care.

Overview

The Principal Data Engineer is a member of the Data Platform team, reporting to the Director of Machine Learning Engineering and Data. The Data Platform team is responsible for executing the data strategy for the organization, ensuring high-quality external data is ingested into the Lakehouse and realized in powerful insights for internal and external customers, while ensuring ML/AI, BI and customer success teams have the data they need to develop and train their models, build insightful dashboards and design semantic layer. As a Principal Data Engineer (L4), you will serve as a key technical leader for CodaMetrix’s Databricks-based data platform, supporting streaming and batch workloads across 30+ healthcare customers. You will own the platform’s architecture, evolution, and operational excellence, including infrastructure-as-code, CI/CD automation, disaster recovery, cost optimization, data contracts, and build-vs-buy decisions, while enabling ML Engineering, Analytics, DevOps, and Product teams and influencing technical standards beyond the immediate team. This role operates with minimal oversight and is expected to influence technical standards beyond the immediate team.

Responsibilities

• Own the technical strategy and roadmap - Own the vision, architecture, and roadmap for the CodaMetrix data platform, ensuring scalability, reliability, regulatory alignment, and operational excellence. Lead key technology decisions, disaster recovery design, architecture reviews, and data engineering standards across teams.

• Platform Engineering at Scale - Design, build, and maintain scalable streaming and batch data platforms Databricks using PySpark, Unity Catalog, Delta Lake, and Structured Streaming. Own Terraform-based infrastructure across environments, including jobs, catalogs, schemas, permissions, compute policies, volumes, and external locations. Build Jenkins CI/CD workflows for automated testing, tagging, and deployments, while optimizing training pipelines, materialized views, retention policies, and production performance.

• Data Governance & Security - Implement and evolve least-privilege access controls across Databricks using Unity Catalog grants, YAML-driven group policies, and schema-level restrictions. Ensure HIPAA and SOC 2 compliance through PHI masking, audit logging, environment-level data segmentation, user provisioning, compute policies, and cost attribution.

• Cost Optimization & Operational Excellence - Drive platform cost reduction through compute policy tuning, serverless optimization, reserved pools, materialized view improvements, and remediation of underutilized resources. Monitor Databricks/AWS spend (using tools like CloudZero and AWS Cost Explorer), own cost attribution and budgeting, resolve production incidents, and maintain runbooks to ensure platform SLAs for uptime and performance.

• Cross-Functional Enablement & Mentorship - Enable ML, BI, Analytics, DevOps, and customer success and implementation teams with training data pipelines, model-ready datasets, feature store architecture, optimized views, dashboards, Tableau refreshes, infrastructure changes, and tenant onboarding. Mentor engineers through code and design reviews, establish quality standards, and serve as a subject matter expert for data platform engineering across the organization.

Requirements

Must-Haves

• A degree in Computer Science or a related field (Bachelor's, Master's, or Ph.D.), or an equivalent combination of education and demonstrable professional experience

• 8+ years of data engineering experience with progressive responsibility

• 5+ years hands-on experience with the Databricks platform (Unity Catalog, Delta Lake, Structured Streaming, Spark SQL, cluster management, platform administration)

• Expert proficiency in PySpark and Python; working knowledge of Scala

• Expert experience with Terraform for infrastructure-as-code (state management, modular structures, multi-environment deployments, YAML-driven configuration)

• Expert experience with Apache Kafka / AWS MSK (streaming ingestion, SASL/IAM auth, topic management, cluster migrations)

• Deep understanding of medallion/lakehouse architecture patterns (bronze/silver/gold, SCD2, materialized views, slowly changing dimensions)

• Proven track record building and maintaining CI/CD pipelines (Jenkins, GitHub Actions) for data platform deployments

• Expert SQL skills — complex CTEs, window functions, performance optimization of large-scale queries across petabyte-scale datasets

• Strong AWS experience (S3, IAM, Secrets Manager, VPC/Private Link, cross-region replication)

• Demonstrated ability to operate in a HIPAA-regulated environment with PHI handling requirements

• Experience with disaster recovery architecture — cross-region replication, failover procedures, read-only replicas

• Experience using AI tools (e.g., Claude, Gemini, Codex) and agentic workflows to augment design, development, and testing processes.

• Huge Plus: Prior experience working with healthcare data, such as medical claims, electronic health records (EHR), or billing code systems (ICD-10, CPT). Familiarity with healthcare data standards like HL7 or FHIR is highly desirable. Track record of influencing technical direction beyond immediate team — architecture reviews, establishing org-widestandards, technology selection. Strategic ability to make build-vs-buy decisions and evaluate emerging technologies for platform evolution

Location: Boston, MA/Remote - Hybrid

Job Type: Full-time, exempt , regular

Compensation: 175,000-200,000

What CodaMetrix can offer you:

Learn more about our full-time employee benefits and how we take care of our team.

• Health Insurance: We cover 80% of the cost of medical and dental insurance and offer vision insurance

• Retirement: We offer a 401(k) plan that eligible employees can contribute to one month after their first day

• Flexibility: We have a generous Paid Time Off policy, which is managed but not limited, so you can take the time you need to relax and rejuvenate

• Development: We provide annual performance evaluations and prioritize working with employees on what their individual growth looks like

• Recognition: We recognize the outstanding achievements of our team through annual company awards where employees have the opportunity to nominate their peers

• Office Location: A modern open plan workspace located in the bustling Back Bay neighborhood of Boston

• Additional Employer Paid Benefits: We offer employer-paid life insurance and short-term and long-term disability insurance

Background Check Notice

All candidates will be required to complete a background check upon acceptance of a job offer.

Equal Employment Opportunity

Our company, as well as our products, are made better because we embrace diverse skills, perspectives, and ideas. CodaMetrix is an Equal Employment Opportunity Employer and all qualified applicants will receive consideration for employment.

Don’t meet every requirement? We invite you to apply anyway. Studies have shown that women, communities of color and historically underrepresented talent are less likely to apply to jobs unless they meet every single qualification. At CodaMetrix we are committed to building a diverse, inclusive and authentic workplace and encourage you to consider joining us.

Why Socure?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

Job Summary:

Socure is the leading provider of digital identity verification and fraud prevention solutions, using AI and machine learning to power accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.

We are seeking a Data Scientist II to join our Digital Intelligence team. In this role, you will develop machine learning features, analytical methods, and production-oriented risk signals using device, network, browser, mobile, API, session, and behavioral telemetry.

This is a hands-on role for a data scientist who can independently deliver well-scoped projects, work with complex and noisy data, and partner with engineering, product, and risk teams to improve fraud detection, identity confidence, and customer outcomes. You will deepen your expertise in Digital Intelligence while contributing to models and signals used in real-world production decisions.

Job Responsibilities:

• Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.

• Work on scoped fraud and identity risk problems where data quality, labels, telemetry coverage, and product tradeoffs need careful analysis.

• Build features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.

• Analyze signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, and device or session fragmentation.

• Design and execute validation analyses, including train/test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.

• Use supervised, unsupervised, statistical, and heuristic approaches to identify durable fraud and identity risk signals.

• Investigate imperfect labels, delayed outcomes, instrumentation gaps, and changing fraud patterns to distinguish useful signal from data artifacts.

• Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.

• Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.

• Communicate methods, assumptions, findings, limitations, and recommendations clearly to technical and cross-functional stakeholders.

• Support junior data scientists and analysts through code review, analytical feedback, and sharing effective modeling and validation practices.

Job Requirements:

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field, or equivalent practical experience.

• 5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role.

• Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals.

• Strong SQL skills and experience working with large-scale, complex datasets.

• Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.

• Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks.

• Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.

• Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions.

• Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact.

• Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use.

• Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders.

• Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions.

Preferred Qualifications:

• Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.

• Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing.

• Experience developing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.

• Familiarity with production ML workflows, model monitoring, feature monitoring, or batch and near-real-time decisioning systems.

• Experience with dashboarding, model explainability, feature documentation, or customer-impact analysis.

• Interest in adversarial behavior, fraud patterns, telemetry quality, and applied ML systems that operate in real-world production environments.

What You’ll Gain

You will work on meaningful data science problems in fraud prevention and identity verification, using high-scale Digital Intelligence telemetry to build features and risk signals that contribute to real-world production decisions.

You will gain deeper experience with device, network, browser, mobile, session, and behavioral intelligence while working closely with senior data scientists, engineering, product, and risk partners. This role offers the opportunity to grow from independently delivering scoped modeling projects toward owning broader workstreams and developing Senior-level technical judgment over time.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.

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Superhuman offers a dynamic hybrid model, and candidates in this role can be based remotely. You may be expected to travel to meet in person during your team’s scheduled collaboration weeks. Managers will determine in-person time according to business needs.

This hybrid approach helps foster trust, innovation, and a strong team culture, with the flexibility of working from home, whenever you need focus time.

About Superhuman

Grammarly is now part of Superhuman, the AI productivity platform on a mission to unlock the superhuman potential in everyone. The Superhuman suite of apps and agents brings AI wherever people work, integrating with over 1 million applications and websites. The company’s products include Grammarly’s writing assistance, Superhuman Docs’ collaborative workspaces, Mail’s inbox management, and Go, the proactive AI assistant that understands context and delivers help automatically. Founded in 2009, Superhuman empowers over 40 million people, 50,000 organizations, and 3,000 educational institutions worldwide to eliminate busywork and focus on what matters. Learn more at superhuman.com and about our values here.

Build Love 💜:

At Superhuman, we have a deep understanding of how to build products that people love. We incorporate fun and play, infusing magic and joy to create experiences that amaze and delight. It all starts with the right team — a team that deeply cares about values, customers, and each other.

Create Massive Impact 🚀:

Our lifecycle marketing engine runs on a foundation of reliable, well-maintained systems. The data pipelines, sync configurations, and technical integrations that power our campaigns don’t manage themselves — and getting them right directly affects how we reach and retain millions of users. This role sits at that foundation, keeping it solid so the broader team can move fast and build with confidence.

The Opportunity

As Lifecycle Marketing Systems Specialist, you will serve as a technical generalist and hands-on executor within Superhuman’s Lifecycle Marketing Operations team, reporting to the Senior Manager, Marketing Systems Architecture.

This is a high-execution, detail-oriented role for someone who thrives in the weeds of martech systems — someone who finds satisfaction in keeping complex platforms running cleanly, resolving the technical friction that slows teams down, and being the connective tissue between marketing and engineering. You won’t just support programs — you’ll own the operational infrastructure that makes them possible and have the opportunity to build your own solutions leveraging the latest AI technologies. .

You’ll take on the day-to-day technical work that keeps our lifecycle systems healthy and our teams unblocked, freeing up senior capacity for strategic and product-level work.

In this role, you will:

• Act as the technical backbone of the lifecycle marketing operations function, owning platform administration and day-to-day execution across martech systems

• Administer and maintain Hightouch as the primary reverse ETL platform — including managing parent models, building and updating audience syncs, reviewing and approving sync requests, and ensuring sync health across destinations

• Serve as the first line of support for martech systems issues, triaging problems, diagnosing root causes, and either resolving them directly or escalating with clear context to engineering

• Partner with engineering and marketing teams to support technical implementations — translating requirements between stakeholders, coordinating handoffs, and helping drive work to completion

• Monitor sync performance and pipeline integrity, proactively catching failures, data inconsistencies, or configuration drift before they impact campaigns

• Maintain and document platform configurations, data models, and operational processes, ensuring the team has reliable references as systems evolve

• Manage access, permissions, and governance across martech platforms, keeping configurations clean and auditable

• Support audience segmentation and data hygiene work in partnership with Data and Lifecycle teams

• Assist with QA and testing for new integrations, campaign launches, and platform changes, helping catch issues before they hit production

• Identify and flag operational inefficiencies, contributing ideas for process improvement and automation opportunities

• Contribute to our team’s evolution by building with AI

Qualifications

• 2–4 years of experience in marketing operations, marketing technology, or a related technical role

• Hands-on experience with reverse ETL or data activation platforms — Hightouch or similar tools (Census, ActionIQ, etc) experience strongly preferred

• Comfortable working directly with data and audiences in a warehouse-connected environment (Databricks, Snowflake, BigQuery, or similar)

• Basic to intermediate SQL skills — able to read, modify, and troubleshoot queries without needing to write complex models from scratch

• Strong systems thinker who can follow a data flow end-to-end and identify where things are breaking down

• Able to communicate technical context clearly across engineering and marketing audiences — you know how to translate

• Detail-oriented with a track record of catching issues before they become incidents

• Experience with marketing automation platforms (Iterable, Braze, Marketo, or similar)

• Familiarity with event streaming concepts (how data flows from product events into marketing platforms) is a plus

• Comfortable in a fast-moving environment where priorities shift and ambiguity is part of the job

• Bonus: Has demonstrated the ability to build solutions with AI applications like Claude

• Has a demonstrated ability to work independently with minimal guidance, proactively manages tasks and priorities across multiple projects, analyzes and executes work efficiently, collaborates effectively with cross-functional teams, and thrives in fast-paced, results-driven environments

Compensation and Benefits

Superhuman offers all team members competitive pay along with a benefits package encompassing the following and more:

• Excellent health care (including a wide range of medical, dental, vision, mental health, and fertility benefits)

• Disability and life insurance options

• 401(k) and RRSP matching

• Paid parental leave

• 20 days of paid time off per year, 12 days of paid holidays per year, two floating holidays per year, and flexible sick time

• Generous stipends (including those for caregiving, pet care, wellness, your home office, and more)

• Annual professional development budget and opportunities

Superhuman takes a market-based approach to compensation, so base pay may vary by location. Our US locations are categorized into two compensation zones based on proximity to our hub locations.

Base pay may vary considerably depending on job-related knowledge, skills, and experience. The expected salary ranges for this position are outlined by compensation zone and may be modified in the future.

We encourage you to apply

At Superhuman, we value our differences, and we encourage all to apply—especially those whose identities are traditionally underrepresented in tech organizations. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, ancestry, national origin, citizenship, age, marital status, veteran status, disability status, political belief, or any other characteristic protected by law. Superhuman is an equal opportunity employer and a participant in the US federal E-Verify program (US). We also abide by the Employment Equity Act (Canada).

Transform healthcare with us.

At Qualified Health, we're redefining what's possible with Generative AI in healthcare. Our infrastructure provides the guardrails for safe AI governance, healthcare-specific agent creation, and real-time algorithm monitoring — working alongside leading health systems to drive real change.

This is more than just a job. It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care. If you're ambitious, innovative, and ready to move fast, we'd love to have you on board.

Join us in shaping the future of healthcare.

About Forward-Deployed Engineering at QH. Data Modernization is a forward-deployed function. Every role on this team — leadership and IC alike — works directly with health system customers, on-site and in their environments, throughout the engagement. This is not a back-office data role: you'll sit with the customer's data and IT teams, present your work to their technical leadership, and be accountable for outcomes they can see. All roles are Senior/Staff level or higher.

Note on platform specialization: We are hiring Senior / Staff Data Engineers who each own deep expertise in one of our three target platforms — Databricks, Snowflake, or Microsoft Fabric. This single posting covers all three seats; we'll match you to the platform where your depth is strongest during the process. The core of the role — landing health system data in a modern lakehouse and serving as the platform-specific technical lead on an engagement — is the same across all three.

Job Summary

This is the role where the data actually moves. As a Senior Forward Deployed Engineer on the Data Modernization team, you own the platform-specific build that takes a health system from legacy connectivity — flat files, manual SFTP, a half-finished Clarity database — to a modern, AI-ready lakehouse that can serve our agentic AI workflows at full speed.

You are the deep platform expert for your stack. During an active engagement, you serve as the platform-specific technical lead under the Principal Solutions Architect: you own the ingestion, the medallion architecture, the governance configuration, and the data-sharing pattern on your platform. Between engagements, you sustain our production environments, build the accelerators and reusable IP that make the next engagement faster, support pre-sales technical discovery, and cross-train on the other platforms so the team stays flexible.

These are time-boxed, high-stakes builds. A greenfield foundation goes from zero to a live AI workflow in roughly ten weeks; an acceleration engagement folds hundreds of Clarity tables into an existing lakehouse on weeks-to-months timelines. You'll ship production-grade work in a regulated environment, where "done" means it's governed, documented, and ready to hand to the integration team — not just that the pipeline ran once.

Key Responsibilities

• Work forward-deployed inside the customer's environment: partner directly with their data and IT teams, present design decisions and progress to their technical leadership, and represent QH on-site during kickoffs and key milestones

• Own platform-specific architecture and build for your stack during active engagements, as technical lead under the Principal Solutions Architect

• Design and implement ingestion from EHR and source systems (Epic Clarity / Caboodle, FHIR, ERP, scheduling, claims) into a medallion lakehouse

• Build and harden change-data-capture, transformation, and orchestration pipelines that meet engagement timelines

• Configure governance, access control, and the data-sharing pattern that hands clean, AI-ready data to QH's platform (Delta Sharing, Fabric External Sharing, Snowflake Reader Accounts, or equivalent)

• Sustain production environments handed off from prior engagements, and develop reusable accelerators and IP that compress the next build

• Support pre-sales technical discovery and source-data assessment alongside the Principal SA

• Ensure every environment meets handoff criteria for the Client Integration team — governed, documented, reproducible

• Cross-train on the other two platforms to keep the team flexible across single- and multi-engagement states

Required Qualifications

• 8+ years in data engineering or data platform roles, at a Senior or Staff IC level

• Client-facing maturity — comfortable working on-site in a customer's environment and presenting technical work to their data and IT leadership

• Deep, hands-on expertise in at least one of Databricks, Snowflake, or Microsoft Fabric (see platform note above)

• Has shipped production data workloads in a regulated environment (HIPAA, HITRUST, or comparable)

• Strong in Python and distributed data processing (PySpark or equivalent), plus SQL and modern transformation tooling

• Comfortable as the sole platform expert on an engagement — you can own a build, not just contribute to one

• Infrastructure-as-code fluency (Terraform) and CI/CD discipline (GitHub Actions)

Platform-Specific Depth (own one)

• Databricks: Delta Lake, Unity Catalog, Delta Sharing, Delta Live Tables, Photon. Bonus: production Databricks experience inside a health system or Databricks partner consultancy.

• Snowflake: Snowpark, Reader Accounts, Streams & Tasks, Dynamic Tables, Snowpipe, strong dbt fluency. Bonus: Epic Clarity inside Snowflake.

• Microsoft Fabric: OneLake, SQL Server Mirroring for CDC, Fabric Data Factory, Fabric External Sharing, Iceberg shortcuts. Bonus: prior Azure Synapse / ADF / Databricks-on-Azure background. (The rarest and most sought-after of the three — Fabric is the newest platform.)

Ideal Experience

• Resident / customer-success solutions architect or engineer from a cloud data platform vendor (Databricks, Snowflake, Microsoft FastTrack / CSU)

• Senior data engineer from a health system running on your platform, or from a platform-partner consultancy

• Familiarity with EHR data models and the realities of on-prem-to-cloud CDC

• Background in consulting, professional services, or data platform implementation in regulated industries (healthcare strongly preferred; fintech a strong adjacent)

Desirable Skills

• Ownership: You're the one person on the engagement who deeply knows this platform, and you carry that weight without needing a second set of hands on every decision.

• Pragmatism: You know the difference between architecturally ideal and deliverable-in-ten-weeks, and you optimize for the latter without creating technical debt.

• Reusability mindset: You build the second engagement's accelerator while delivering the first, because you've felt the cost of bespoke-everything.

• Clinical-data literacy: Clarity and Caboodle don't scare you; you understand why health system data is messy and you've untangled it before.

• Cross-platform curiosity: Your depth is in one stack, but you're glad to learn the other two so the team can flex across engagements.

Technical Environment

• Databricks (primary), Microsoft Fabric, and Snowflake — fully platform-agnostic on acceleration engagements

• PySpark and Python with type-safe patterns and modern frameworks

• GitHub Actions + Terraform for CI/CD and Infrastructure as Code

• Healthcare data formats including FHIR, Epic Clarity / Caboodle, and other EHR schemas

• HIPAA / HITRUST-regulated cloud environments on Azure and AWS

Why Join Qualified Health?

This is an opportunity to join a fast-growing company and a world-class team that is poised to change the healthcare industry. We are a passionate, mission-driven team that is building a category-defining product. We are backed by premier investors and are looking for founding team members who are excited to do the best work of their careers.

The work here is unusually high-leverage: every health system you modernize removes the single biggest bottleneck to deploying AI across our entire partner base. You'll be the named platform expert on real, in-flight engagements — not one engineer of fifty, but the person who owns the build.

Our employees are integral to achieving our goals, so we are proud to offer competitive salaries with equity packages, robust medical/dental/vision insurance, flexible working hours, hybrid work options, and an inclusive environment that fosters creativity and innovation.

Our Commitment to Diversity

Qualified Health is an equal opportunity employer. We believe that a diverse and inclusive workplace is essential to our success, and we are committed to building a team that reflects the world we live in. We encourage applications from all qualified individuals, regardless of race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, marital status, disability, or veteran status.

Pay & Benefits

The base pay range for this role is between $180,000 and $230,000. Final offer depends on your skills, qualifications, experience, platform specialization, and location. This role is also eligible for equity, benefits, unlimited PTO.